[1]章杭奎,刘栋军,孔万增.面向跨被试RSVP的多特征低维子空间嵌入的ERP检测[J].智能系统学报,2022,17(5):1054-1061.[doi:10.11992/tis.202111059]
ZHANG Hangkui,LIU Dongjun,KONG Wanzeng.ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP[J].CAAI Transactions on Intelligent Systems,2022,17(5):1054-1061.[doi:10.11992/tis.202111059]
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ZHANG Hangkui,LIU Dongjun,KONG Wanzeng.ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP[J].CAAI Transactions on Intelligent Systems,2022,17(5):1054-1061.[doi:10.11992/tis.202111059]
面向跨被试RSVP的多特征低维子空间嵌入的ERP检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第5期
页码:
1054-1061
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2022-09-05
- Title:
- ERP detection of multi-feature embedding in the low-dimensional subspace for cross-subject RSVP
- Keywords:
- rapid serial visual presentation; event-related potential; Euclidean space data alignment; cross-subject; multiple features; low-dimensional subspace embedding; leave-one-subject-out
- 分类号:
- TP18
- 文献标志码:
- 2022-06-20
- 摘要:
- 基于快速序列视觉呈现（rapid serial visual presentation, RSVP）范式的目标图像检索借助于人脑在看到目标图像时产生的事件相关电位（event-related potentials, ERP）来完成复杂目标图像检索。在应用RSVP范式进行复杂目标图像检索时存在跨时段甚至跨被试的问题。对此，本文提出了一种面向跨被试RSVP的多特征低维子空间嵌入的ERP检测方法，首先采用迁移学习方法中的欧式空间对齐对不同被试的数据进行对齐，其次将来自不同空间的特征分别进行有监督降维、重构。最终采用留一被试法作为检验方法、平衡准确率作为评价指标，在PhysioNetRSVP数据集以及清华RSVP数据集下共计14个长度分段中，有12个长度分段达到最优分类结果。结果表明本文提出的多特征低维子空间嵌入方法能够有效提升ERP检测时的稳定性。
- Abstract:
- The rapid serial visual presentation (RSVP)-based target image retrieval method finishes complex target image retrieval by relying on the event-related potentials (ERP) generated by the human brain when noticing a target image. When applying the RSVP paradigm to complex target image retrieval, the problems of cross-period and even cross-subjects often arise. To solve these problems, this paper proposes an ERP detection method of multi-feature embedding in a low-dimensional subspace for cross-subject RSVP. First, the Euclidean space data alignment in the transfer learning method is used to align the EEG data. Then, supervised dimensionality reduction and reconstruction are conducted on features from different spaces, respectively. Finally, the leave-one-subject-out method is used as the test method and the balanced classification accuracy rate as the evaluation indicator. Consequently, out of 14 length segments under the PhysioNetRSVP dataset and the Tsinghua RSVP dataset, 12 length segments achieve optimal classification results. Experimental results show that multi-feature embedding in the low-dimensional subspace proposed in this paper can effectively improve the stability of ERP detection.
- 参考文献/References:
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备注/Memo
收稿日期:2021-11-30。
基金项目:国家重点研发计划项目（2017YFE0116800）；国家自然科学基金项目（U20B2074，U1909202）
作者简介:章杭奎，硕士研究生，主要研究方向为脑机协同图像检索、跨被试事件相关电位检测;刘栋军，硕士研究生，主要研究方向为脑机协同智能、脑电迁移学习;孔万增，教授、博士生导师，博士，主要研究方向为人工智能与模式识别、嵌入式可穿戴计算、脑机交互与认知计算。2018年度吴文俊人工智能技术发明奖获得者。发表学术论文近百篇
通讯作者:孔万增. E-mail：kongwanzeng@hdu.edu.cn
更新日期/Last Update:
1900-01-01